Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations46925
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory83.0 B

Variable types

Categorical3
Numeric10

Alerts

emg_10 is highly overall correlated with emg_6 and 2 other fieldsHigh correlation
emg_2 is highly overall correlated with emg_3 and 3 other fieldsHigh correlation
emg_3 is highly overall correlated with emg_2 and 2 other fieldsHigh correlation
emg_4 is highly overall correlated with emg_2 and 3 other fieldsHigh correlation
emg_5 is highly overall correlated with emg_4 and 2 other fieldsHigh correlation
emg_6 is highly overall correlated with emg_10 and 3 other fieldsHigh correlation
emg_7 is highly overall correlated with emg_10 and 2 other fieldsHigh correlation
emg_8 is highly overall correlated with emg_10 and 5 other fieldsHigh correlation
emg_9 is highly overall correlated with emg_2 and 3 other fieldsHigh correlation
s is uniformly distributed Uniform
emg_1 has unique values Unique
emg_3 has unique values Unique
emg_4 has unique values Unique
emg_5 has unique values Unique
emg_7 has unique values Unique
emg_8 has unique values Unique
emg_9 has unique values Unique
emg_10 has unique values Unique

Reproduction

Analysis started2024-11-05 02:05:13.558275
Analysis finished2024-11-05 02:05:34.143078
Duration20.58 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

s
Categorical

Uniform 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.3 KiB
6
4712 
1
4705 
8
4705 
9
4697 
5
4692 
Other values (5)
23414 

Length

Max length2
Median length1
Mean length1.0999254
Min length1

Characters and Unicode

Total characters51614
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
6 4712
10.0%
1 4705
10.0%
8 4705
10.0%
9 4697
10.0%
5 4692
10.0%
7 4689
10.0%
10 4689
10.0%
4 4687
10.0%
3 4684
10.0%
2 4665
9.9%

Length

2024-11-04T21:05:34.592674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T21:05:34.844896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
6 4712
10.0%
1 4705
10.0%
8 4705
10.0%
9 4697
10.0%
5 4692
10.0%
7 4689
10.0%
10 4689
10.0%
4 4687
10.0%
3 4684
10.0%
2 4665
9.9%

Most occurring characters

ValueCountFrequency (%)
1 9394
18.2%
6 4712
9.1%
8 4705
9.1%
9 4697
9.1%
5 4692
9.1%
7 4689
9.1%
0 4689
9.1%
4 4687
9.1%
3 4684
9.1%
2 4665
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9394
18.2%
6 4712
9.1%
8 4705
9.1%
9 4697
9.1%
5 4692
9.1%
7 4689
9.1%
0 4689
9.1%
4 4687
9.1%
3 4684
9.1%
2 4665
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9394
18.2%
6 4712
9.1%
8 4705
9.1%
9 4697
9.1%
5 4692
9.1%
7 4689
9.1%
0 4689
9.1%
4 4687
9.1%
3 4684
9.1%
2 4665
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9394
18.2%
6 4712
9.1%
8 4705
9.1%
9 4697
9.1%
5 4692
9.1%
7 4689
9.1%
0 4689
9.1%
4 4687
9.1%
3 4684
9.1%
2 4665
9.0%

emg_1
Real number (ℝ)

Unique 

Distinct46925
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.071379769
Minimum0.00038506379
Maximum3.0292888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2024-11-04T21:05:35.059117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00038506379
5-th percentile0.0024566142
Q10.0029413393
median0.0054241294
Q30.021904421
95-th percentile0.43475952
Maximum3.0292888
Range3.0289037
Interquartile range (IQR)0.018963081

Descriptive statistics

Standard deviation0.2151937
Coefficient of variation (CV)3.0147716
Kurtosis29.573764
Mean0.071379769
Median Absolute Deviation (MAD)0.0029113336
Skewness4.9409719
Sum3349.4957
Variance0.046308327
MonotonicityNot monotonic
2024-11-04T21:05:35.261899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05251036743 1
 
< 0.1%
0.003700673802 1
 
< 0.1%
0.002926303826 1
 
< 0.1%
0.003601476763 1
 
< 0.1%
0.004414904142 1
 
< 0.1%
0.004600829323 1
 
< 0.1%
0.004066635332 1
 
< 0.1%
0.003691095875 1
 
< 0.1%
0.003995619509 1
 
< 0.1%
0.003952054082 1
 
< 0.1%
Other values (46915) 46915
> 99.9%
ValueCountFrequency (%)
0.0003850637884 1
< 0.1%
0.0004163248092 1
< 0.1%
0.0004462171093 1
< 0.1%
0.0004833341768 1
< 0.1%
0.0004962270972 1
< 0.1%
0.0005150261395 1
< 0.1%
0.0005260852844 1
< 0.1%
0.0005278045006 1
< 0.1%
0.0005574768896 1
< 0.1%
0.0005715279567 1
< 0.1%
ValueCountFrequency (%)
3.029288785 1
< 0.1%
2.913799604 1
< 0.1%
2.827160218 1
< 0.1%
2.771108093 1
< 0.1%
2.722615609 1
< 0.1%
2.678131312 1
< 0.1%
2.612801235 1
< 0.1%
2.572053211 1
< 0.1%
2.468325123 1
< 0.1%
2.442389494 1
< 0.1%

emg_2
Real number (ℝ)

High correlation 

Distinct46916
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12508645
Minimum0.00030630421
Maximum2.9532567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2024-11-04T21:05:35.458383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00030630421
5-th percentile0.0022256962
Q10.0024064784
median0.010433639
Q30.16938438
95-th percentile0.60461042
Maximum2.9532567
Range2.9529504
Interquartile range (IQR)0.1669779

Descriptive statistics

Standard deviation0.22008552
Coefficient of variation (CV)1.7594672
Kurtosis13.448395
Mean0.12508645
Median Absolute Deviation (MAD)0.0084363401
Skewness2.9685103
Sum5869.6818
Variance0.048437634
MonotonicityNot monotonic
2024-11-04T21:05:35.644957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0024 4
 
< 0.1%
0.0024 3
 
< 0.1%
0.0024 3
 
< 0.1%
0.0024 2
 
< 0.1%
0.0024 2
 
< 0.1%
0.002381081567 1
 
< 0.1%
0.002227728567 1
 
< 0.1%
0.002029951218 1
 
< 0.1%
0.00238378309 1
 
< 0.1%
0.004703922988 1
 
< 0.1%
Other values (46906) 46906
> 99.9%
ValueCountFrequency (%)
0.0003063042088 1
< 0.1%
0.0003299535558 1
< 0.1%
0.0003307753921 1
< 0.1%
0.0003432827894 1
< 0.1%
0.0003457598028 1
< 0.1%
0.0003463837726 1
< 0.1%
0.0003509374159 1
< 0.1%
0.0003565212731 1
< 0.1%
0.0003594827412 1
< 0.1%
0.0003601352411 1
< 0.1%
ValueCountFrequency (%)
2.9532567 1
< 0.1%
2.874799637 1
< 0.1%
2.789485422 1
< 0.1%
2.638284082 1
< 0.1%
2.581502894 1
< 0.1%
2.544331203 1
< 0.1%
2.459190393 1
< 0.1%
2.437164316 1
< 0.1%
2.402320514 1
< 0.1%
2.353486274 1
< 0.1%

emg_3
Real number (ℝ)

High correlation  Unique 

Distinct46925
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.095337602
Minimum0.00021678646
Maximum4.4571686
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2024-11-04T21:05:35.844624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00021678646
5-th percentile0.0023267481
Q10.0025419148
median0.0044781992
Q30.079823641
95-th percentile0.44389238
Maximum4.4571686
Range4.4569518
Interquartile range (IQR)0.077281726

Descriptive statistics

Standard deviation0.25453996
Coefficient of variation (CV)2.66988
Kurtosis71.081106
Mean0.095337602
Median Absolute Deviation (MAD)0.0024195866
Skewness6.9636847
Sum4473.717
Variance0.06479059
MonotonicityNot monotonic
2024-11-04T21:05:36.023231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002444820033 1
 
< 0.1%
0.002395555096 1
 
< 0.1%
0.006846545444 1
 
< 0.1%
0.01399398041 1
 
< 0.1%
0.01990067813 1
 
< 0.1%
0.02188759893 1
 
< 0.1%
0.02761104497 1
 
< 0.1%
0.04068392452 1
 
< 0.1%
0.0520400907 1
 
< 0.1%
0.04880090048 1
 
< 0.1%
Other values (46915) 46915
> 99.9%
ValueCountFrequency (%)
0.0002167864576 1
< 0.1%
0.0003364851686 1
< 0.1%
0.0003417696913 1
< 0.1%
0.0003423768781 1
< 0.1%
0.0003494595793 1
< 0.1%
0.000356692511 1
< 0.1%
0.0003631127865 1
< 0.1%
0.0003675284986 1
< 0.1%
0.000367983935 1
< 0.1%
0.0003740912612 1
< 0.1%
ValueCountFrequency (%)
4.457168568 1
< 0.1%
4.375157191 1
< 0.1%
4.313034758 1
< 0.1%
4.280158854 1
< 0.1%
4.234338036 1
< 0.1%
4.183457188 1
< 0.1%
4.169742904 1
< 0.1%
4.162974573 1
< 0.1%
4.144272596 1
< 0.1%
4.072874395 1
< 0.1%

emg_4
Real number (ℝ)

High correlation  Unique 

Distinct46925
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.047645268
Minimum0.00031564749
Maximum4.6427728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2024-11-04T21:05:36.274951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00031564749
5-th percentile0.0022178614
Q10.002407031
median0.0024688437
Q30.014651765
95-th percentile0.25646153
Maximum4.6427728
Range4.6424571
Interquartile range (IQR)0.012244734

Descriptive statistics

Standard deviation0.15963546
Coefficient of variation (CV)3.3504997
Kurtosis133.48371
Mean0.047645268
Median Absolute Deviation (MAD)7.4799788 × 10-5
Skewness9.0632849
Sum2235.7542
Variance0.025483479
MonotonicityNot monotonic
2024-11-04T21:05:36.455476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002417462869 1
 
< 0.1%
0.002438582884 1
 
< 0.1%
0.002397009878 1
 
< 0.1%
0.002396129616 1
 
< 0.1%
0.002401128524 1
 
< 0.1%
0.00241790513 1
 
< 0.1%
0.002444635836 1
 
< 0.1%
0.00245330779 1
 
< 0.1%
0.002450092853 1
 
< 0.1%
0.00244840034 1
 
< 0.1%
Other values (46915) 46915
> 99.9%
ValueCountFrequency (%)
0.000315647495 1
< 0.1%
0.0003227855909 1
< 0.1%
0.0003375141453 1
< 0.1%
0.0003513336226 1
< 0.1%
0.0003539824045 1
< 0.1%
0.0003593955429 1
< 0.1%
0.0003603181891 1
< 0.1%
0.0003639443158 1
< 0.1%
0.0003653602805 1
< 0.1%
0.0003696037571 1
< 0.1%
ValueCountFrequency (%)
4.642772765 1
< 0.1%
4.34268385 1
< 0.1%
4.231194524 1
< 0.1%
3.368064167 1
< 0.1%
3.331795166 1
< 0.1%
3.2996256 1
< 0.1%
3.277047436 1
< 0.1%
3.272277985 1
< 0.1%
3.251347941 1
< 0.1%
3.19789678 1
< 0.1%

emg_5
Real number (ℝ)

High correlation  Unique 

Distinct46925
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01321152
Minimum0.00035494425
Maximum0.85219955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2024-11-04T21:05:36.656670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00035494425
5-th percentile0.0023621446
Q10.0024921782
median0.0025811897
Q30.0040822108
95-th percentile0.070134731
Maximum0.85219955
Range0.8518446
Interquartile range (IQR)0.0015900326

Descriptive statistics

Standard deviation0.034173429
Coefficient of variation (CV)2.5866386
Kurtosis57.162325
Mean0.01321152
Median Absolute Deviation (MAD)0.00012386568
Skewness6.0125433
Sum619.95059
Variance0.0011678232
MonotonicityNot monotonic
2024-11-04T21:05:36.841823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002400290228 1
 
< 0.1%
0.002443867207 1
 
< 0.1%
0.003389939084 1
 
< 0.1%
0.003859105741 1
 
< 0.1%
0.003608550737 1
 
< 0.1%
0.003143619135 1
 
< 0.1%
0.002987143482 1
 
< 0.1%
0.002993098095 1
 
< 0.1%
0.002879315026 1
 
< 0.1%
0.002778109235 1
 
< 0.1%
Other values (46915) 46915
> 99.9%
ValueCountFrequency (%)
0.0003549442487 1
< 0.1%
0.0003612062971 1
< 0.1%
0.000362371442 1
< 0.1%
0.0003840384094 1
< 0.1%
0.0003861056575 1
< 0.1%
0.0003917252546 1
< 0.1%
0.0003927521651 1
< 0.1%
0.0003960075334 1
< 0.1%
0.0003978460787 1
< 0.1%
0.000409426709 1
< 0.1%
ValueCountFrequency (%)
0.8521995463 1
< 0.1%
0.7921556163 1
< 0.1%
0.7545193097 1
< 0.1%
0.5847882324 1
< 0.1%
0.5512956231 1
< 0.1%
0.5183322676 1
< 0.1%
0.5064197363 1
< 0.1%
0.4947957383 1
< 0.1%
0.4814140426 1
< 0.1%
0.4757513054 1
< 0.1%

emg_6
Real number (ℝ)

High correlation 

Distinct46886
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041202972
Minimum0.00035044741
Maximum1.8909136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2024-11-04T21:05:37.027216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00035044741
5-th percentile0.0023426615
Q10.0024026246
median0.0035379275
Q30.029006824
95-th percentile0.21122016
Maximum1.8909136
Range1.8905631
Interquartile range (IQR)0.0266042

Descriptive statistics

Standard deviation0.10713771
Coefficient of variation (CV)2.6002423
Kurtosis59.035385
Mean0.041202972
Median Absolute Deviation (MAD)0.0012478001
Skewness6.2191329
Sum1933.4495
Variance0.011478489
MonotonicityNot monotonic
2024-11-04T21:05:37.227539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0024 30
 
0.1%
0.0024 5
 
< 0.1%
0.0024 2
 
< 0.1%
0.0024 2
 
< 0.1%
0.0024 2
 
< 0.1%
0.0024 2
 
< 0.1%
0.0024 2
 
< 0.1%
0.0024 2
 
< 0.1%
0.6853587907 1
 
< 0.1%
0.006911872162 1
 
< 0.1%
Other values (46876) 46876
99.9%
ValueCountFrequency (%)
0.0003504474071 1
< 0.1%
0.0003508484715 1
< 0.1%
0.0003601166536 1
< 0.1%
0.0003889278374 1
< 0.1%
0.000390479938 1
< 0.1%
0.000396571117 1
< 0.1%
0.0004003505141 1
< 0.1%
0.0004100887889 1
< 0.1%
0.0004404039399 1
< 0.1%
0.0004438629953 1
< 0.1%
ValueCountFrequency (%)
1.890913565 1
< 0.1%
1.858276934 1
< 0.1%
1.855175658 1
< 0.1%
1.813730121 1
< 0.1%
1.792519883 1
< 0.1%
1.737719746 1
< 0.1%
1.734108102 1
< 0.1%
1.718346548 1
< 0.1%
1.706997513 1
< 0.1%
1.674128539 1
< 0.1%

emg_7
Real number (ℝ)

High correlation  Unique 

Distinct46925
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26883052
Minimum0.00032793876
Maximum4.7686651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2024-11-04T21:05:37.525070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00032793876
5-th percentile0.0024011284
Q10.017919156
median0.13704517
Q30.3434689
95-th percentile0.91123254
Maximum4.7686651
Range4.7683371
Interquartile range (IQR)0.32554974

Descriptive statistics

Standard deviation0.44300351
Coefficient of variation (CV)1.6478914
Kurtosis33.763237
Mean0.26883052
Median Absolute Deviation (MAD)0.13264742
Skewness4.8349465
Sum12614.872
Variance0.19625211
MonotonicityNot monotonic
2024-11-04T21:05:37.743580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0023999824 1
 
< 0.1%
0.307265991 1
 
< 0.1%
0.3117740796 1
 
< 0.1%
0.3151475797 1
 
< 0.1%
0.3284186303 1
 
< 0.1%
0.3436613921 1
 
< 0.1%
0.3509430367 1
 
< 0.1%
0.3516200813 1
 
< 0.1%
0.3533628825 1
 
< 0.1%
0.3671999245 1
 
< 0.1%
Other values (46915) 46915
> 99.9%
ValueCountFrequency (%)
0.0003279387643 1
< 0.1%
0.0003352402758 1
< 0.1%
0.0003729703225 1
< 0.1%
0.0003927937518 1
< 0.1%
0.0003990595556 1
< 0.1%
0.0003995174261 1
< 0.1%
0.0004029540114 1
< 0.1%
0.0004069849612 1
< 0.1%
0.0004133079214 1
< 0.1%
0.0004186224542 1
< 0.1%
ValueCountFrequency (%)
4.768665067 1
< 0.1%
4.763085993 1
< 0.1%
4.761005599 1
< 0.1%
4.750531399 1
< 0.1%
4.747814285 1
< 0.1%
4.743846871 1
< 0.1%
4.743039627 1
< 0.1%
4.740039234 1
< 0.1%
4.739789639 1
< 0.1%
4.736503785 1
< 0.1%

emg_8
Real number (ℝ)

High correlation  Unique 

Distinct46925
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2263592
Minimum0.00032605532
Maximum4.3554817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2024-11-04T21:05:38.124850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00032605532
5-th percentile0.0045178598
Q10.049770877
median0.11734335
Q30.28380152
95-th percentile0.78606217
Maximum4.3554817
Range4.3551556
Interquartile range (IQR)0.23403064

Descriptive statistics

Standard deviation0.31949561
Coefficient of variation (CV)1.411454
Kurtosis27.063648
Mean0.2263592
Median Absolute Deviation (MAD)0.087686674
Skewness4.0767835
Sum10621.906
Variance0.10207744
MonotonicityNot monotonic
2024-11-04T21:05:38.293858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04121818382 1
 
< 0.1%
0.1454684343 1
 
< 0.1%
0.1994993047 1
 
< 0.1%
0.2280789946 1
 
< 0.1%
0.2690505248 1
 
< 0.1%
0.3029723988 1
 
< 0.1%
0.3221295556 1
 
< 0.1%
0.3086824142 1
 
< 0.1%
0.2834929364 1
 
< 0.1%
0.2706527339 1
 
< 0.1%
Other values (46915) 46915
> 99.9%
ValueCountFrequency (%)
0.0003260553198 1
< 0.1%
0.0003675279028 1
< 0.1%
0.0004302160982 1
< 0.1%
0.0004876391842 1
< 0.1%
0.0004944153201 1
< 0.1%
0.0004975328688 1
< 0.1%
0.000510337438 1
< 0.1%
0.0005327721864 1
< 0.1%
0.0005482346606 1
< 0.1%
0.0005542727006 1
< 0.1%
ValueCountFrequency (%)
4.355481693 1
< 0.1%
4.301523004 1
< 0.1%
4.269156561 1
< 0.1%
4.256309494 1
< 0.1%
4.186045114 1
< 0.1%
4.164058893 1
< 0.1%
4.152429394 1
< 0.1%
4.149732519 1
< 0.1%
4.136880107 1
< 0.1%
4.127196136 1
< 0.1%

emg_9
Real number (ℝ)

High correlation  Unique 

Distinct46925
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13589195
Minimum0.00019504363
Maximum4.0155122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2024-11-04T21:05:38.526519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00019504363
5-th percentile0.0021325346
Q10.0024241277
median0.011073421
Q30.13196848
95-th percentile0.68063629
Maximum4.0155122
Range4.0153172
Interquartile range (IQR)0.12954436

Descriptive statistics

Standard deviation0.29741334
Coefficient of variation (CV)2.1886016
Kurtosis31.114163
Mean0.13589195
Median Absolute Deviation (MAD)0.0090466542
Skewness4.5873294
Sum6376.7299
Variance0.088454696
MonotonicityNot monotonic
2024-11-04T21:05:38.741627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002400000049 1
 
< 0.1%
0.002416066193 1
 
< 0.1%
0.00439471769 1
 
< 0.1%
0.0080277057 1
 
< 0.1%
0.01118096977 1
 
< 0.1%
0.01409725621 1
 
< 0.1%
0.02138556363 1
 
< 0.1%
0.0322592841 1
 
< 0.1%
0.04283572056 1
 
< 0.1%
0.04688438659 1
 
< 0.1%
Other values (46915) 46915
> 99.9%
ValueCountFrequency (%)
0.0001950436326 1
< 0.1%
0.0003037295957 1
< 0.1%
0.0003149290011 1
< 0.1%
0.0003175877239 1
< 0.1%
0.0003245198854 1
< 0.1%
0.0003400362162 1
< 0.1%
0.0003403504315 1
< 0.1%
0.0003465083503 1
< 0.1%
0.0003480849939 1
< 0.1%
0.0003529801315 1
< 0.1%
ValueCountFrequency (%)
4.015512249 1
< 0.1%
3.945802939 1
< 0.1%
3.8889382 1
< 0.1%
3.885325022 1
< 0.1%
3.771274674 1
< 0.1%
3.765199932 1
< 0.1%
3.748909936 1
< 0.1%
3.711897442 1
< 0.1%
3.583225472 1
< 0.1%
3.559572585 1
< 0.1%

emg_10
Real number (ℝ)

High correlation  Unique 

Distinct46925
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18947568
Minimum0.00029587002
Maximum2.8972566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2024-11-04T21:05:38.958781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00029587002
5-th percentile0.0023910523
Q10.0046721901
median0.062891293
Q30.25306725
95-th percentile0.77341557
Maximum2.8972566
Range2.8969607
Interquartile range (IQR)0.24839506

Descriptive statistics

Standard deviation0.29636904
Coefficient of variation (CV)1.5641535
Kurtosis12.079607
Mean0.18947568
Median Absolute Deviation (MAD)0.060488652
Skewness2.9494322
Sum8891.1462
Variance0.087834608
MonotonicityNot monotonic
2024-11-04T21:05:39.143997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01952624971 1
 
< 0.1%
0.2358085809 1
 
< 0.1%
0.2258036967 1
 
< 0.1%
0.2320175954 1
 
< 0.1%
0.2381800338 1
 
< 0.1%
0.2406449114 1
 
< 0.1%
0.2401343011 1
 
< 0.1%
0.2406128274 1
 
< 0.1%
0.2429386279 1
 
< 0.1%
0.2462637543 1
 
< 0.1%
Other values (46915) 46915
> 99.9%
ValueCountFrequency (%)
0.0002958700156 1
< 0.1%
0.0003116952215 1
< 0.1%
0.0003373622748 1
< 0.1%
0.0003389382712 1
< 0.1%
0.0003417921287 1
< 0.1%
0.0003417966178 1
< 0.1%
0.0003583620045 1
< 0.1%
0.0003593794004 1
< 0.1%
0.0003595860124 1
< 0.1%
0.000362045679 1
< 0.1%
ValueCountFrequency (%)
2.89725661 1
< 0.1%
2.797050905 1
< 0.1%
2.790236171 1
< 0.1%
2.779434956 1
< 0.1%
2.756451717 1
< 0.1%
2.705184556 1
< 0.1%
2.695463065 1
< 0.1%
2.676278486 1
< 0.1%
2.668264539 1
< 0.1%
2.656468649 1
< 0.1%

rep
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.3 KiB
0
26853 
1
 
2128
2
 
2084
3
 
2051
5
 
2043
Other values (6)
11766 

Length

Max length2
Median length1
Mean length1.0411295
Min length1

Characters and Unicode

Total characters48855
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26853
57.2%
1 2128
 
4.5%
2 2084
 
4.4%
3 2051
 
4.4%
5 2043
 
4.4%
6 1983
 
4.2%
4 1980
 
4.2%
8 1979
 
4.2%
9 1958
 
4.2%
7 1936
 
4.1%

Length

2024-11-04T21:05:39.309373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 26853
57.2%
1 2128
 
4.5%
2 2084
 
4.4%
3 2051
 
4.4%
5 2043
 
4.4%
6 1983
 
4.2%
4 1980
 
4.2%
8 1979
 
4.2%
9 1958
 
4.2%
7 1936
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 28783
58.9%
1 4058
 
8.3%
2 2084
 
4.3%
3 2051
 
4.2%
5 2043
 
4.2%
6 1983
 
4.1%
4 1980
 
4.1%
8 1979
 
4.1%
9 1958
 
4.0%
7 1936
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28783
58.9%
1 4058
 
8.3%
2 2084
 
4.3%
3 2051
 
4.2%
5 2043
 
4.2%
6 1983
 
4.1%
4 1980
 
4.1%
8 1979
 
4.1%
9 1958
 
4.0%
7 1936
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28783
58.9%
1 4058
 
8.3%
2 2084
 
4.3%
3 2051
 
4.2%
5 2043
 
4.2%
6 1983
 
4.1%
4 1980
 
4.1%
8 1979
 
4.1%
9 1958
 
4.0%
7 1936
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28783
58.9%
1 4058
 
8.3%
2 2084
 
4.3%
3 2051
 
4.2%
5 2043
 
4.2%
6 1983
 
4.1%
4 1980
 
4.1%
8 1979
 
4.1%
9 1958
 
4.0%
7 1936
 
4.0%

label
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.6 KiB
0
26853 
3
 
1876
8
 
1776
1
 
1747
5
 
1703
Other values (8)
12970 

Length

Max length2
Median length1
Mean length1.1047629
Min length1

Characters and Unicode

Total characters51841
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26853
57.2%
3 1876
 
4.0%
8 1776
 
3.8%
1 1747
 
3.7%
5 1703
 
3.6%
7 1697
 
3.6%
12 1686
 
3.6%
10 1684
 
3.6%
6 1652
 
3.5%
2 1629
 
3.5%
Other values (3) 4622
 
9.8%

Length

2024-11-04T21:05:39.459740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 26853
57.2%
3 1876
 
4.0%
8 1776
 
3.8%
1 1747
 
3.7%
5 1703
 
3.6%
7 1697
 
3.6%
12 1686
 
3.6%
10 1684
 
3.6%
6 1652
 
3.5%
2 1629
 
3.5%
Other values (3) 4622
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 28537
55.0%
1 8209
 
15.8%
2 3315
 
6.4%
3 1876
 
3.6%
8 1776
 
3.4%
5 1703
 
3.3%
7 1697
 
3.3%
6 1652
 
3.2%
4 1556
 
3.0%
9 1520
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51841
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28537
55.0%
1 8209
 
15.8%
2 3315
 
6.4%
3 1876
 
3.6%
8 1776
 
3.4%
5 1703
 
3.3%
7 1697
 
3.3%
6 1652
 
3.2%
4 1556
 
3.0%
9 1520
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51841
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28537
55.0%
1 8209
 
15.8%
2 3315
 
6.4%
3 1876
 
3.6%
8 1776
 
3.4%
5 1703
 
3.3%
7 1697
 
3.3%
6 1652
 
3.2%
4 1556
 
3.0%
9 1520
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51841
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28537
55.0%
1 8209
 
15.8%
2 3315
 
6.4%
3 1876
 
3.6%
8 1776
 
3.4%
5 1703
 
3.3%
7 1697
 
3.3%
6 1652
 
3.2%
4 1556
 
3.0%
9 1520
 
2.9%

Interactions

2024-11-04T21:05:31.650082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:15.425658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:17.408625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:19.308488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:21.148087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:22.762397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:24.413452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:26.246616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:28.108189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:29.844372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:31.927061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:15.608726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:17.574323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:19.474454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:21.293557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:22.943841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:24.610721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:26.394054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:28.327844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:30.009508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:32.093260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:15.780323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:17.744034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:19.683802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:21.459955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:23.093177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:24.830530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:26.575825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:28.497000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:30.209694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:32.244106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:15.962425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:17.893068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:19.844398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:21.624417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:23.262807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:24.995863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:26.726604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:28.627519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:30.360676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:32.408667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:16.244560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:18.141471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:19.976910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:21.778324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:23.408462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:25.177714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:26.881794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:28.777406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:30.527519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:32.581970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:16.443967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:18.445070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:20.143566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:21.928156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:23.577888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:25.342054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:27.027276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:28.948411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:30.693113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:32.758293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:16.623137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:18.626419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:20.310230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:22.093682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:23.743139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:25.528255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:27.229598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:29.092990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:30.875980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:32.925610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:16.777209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:18.794066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:20.459945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:22.242969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:23.910256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:25.713122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:27.393741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:29.265066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:31.099341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:33.076940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:16.960365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:18.944012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:20.643937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:22.408491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:24.060478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:25.874152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:27.725018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:29.423455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:31.260756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:33.275953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:17.243201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:19.128549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:20.977147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:22.576562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:24.244227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:26.059941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:27.911518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:29.662296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-04T21:05:31.459356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-11-04T21:05:39.644572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
emg_1emg_10emg_2emg_3emg_4emg_5emg_6emg_7emg_8emg_9labelreps
emg_11.000-0.1470.1590.2160.130-0.010-0.084-0.133-0.0720.1760.1220.0880.173
emg_10-0.1471.0000.4110.3320.3930.4800.6350.6180.6980.4650.1780.1210.171
emg_20.1590.4111.0000.7310.5680.4720.3300.2460.5090.6790.1560.1190.132
emg_30.2160.3320.7311.0000.6820.4610.2590.2150.4770.7420.1550.0920.088
emg_40.1300.3930.5680.6821.0000.5360.3250.2300.4890.6410.1240.0630.087
emg_5-0.0100.4800.4720.4610.5361.0000.5840.4330.5190.4920.1250.0830.082
emg_6-0.0840.6350.3300.2590.3250.5841.0000.7730.5580.3650.1250.0680.146
emg_7-0.1330.6180.2460.2150.2300.4330.7731.0000.6250.3100.1970.1200.139
emg_8-0.0720.6980.5090.4770.4890.5190.5580.6251.0000.5360.1680.1290.117
emg_90.1760.4650.6790.7420.6410.4920.3650.3100.5361.0000.1630.1130.108
label0.1220.1780.1560.1550.1240.1250.1250.1970.1680.1631.0000.3170.046
rep0.0880.1210.1190.0920.0630.0830.0680.1200.1290.1130.3171.0000.036
s0.1730.1710.1320.0880.0870.0820.1460.1390.1170.1080.0460.0361.000

Missing values

2024-11-04T21:05:33.508208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-04T21:05:33.874771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

semg_1emg_2emg_3emg_4emg_5emg_6emg_7emg_8emg_9emg_10replabel
010.0525100.0024140.0024450.0024170.0024000.0062040.0024000.0412180.00240.01952600
110.0385430.0024400.0025130.0024430.0024260.0028030.0024000.0297890.00240.00503500
210.0356620.0024480.0025640.0024460.0024780.0019750.0024000.0252870.00240.00081300
310.0370380.0024250.0025420.0024200.0025260.0021290.0024000.0262160.00240.00148500
410.0357180.0024040.0024780.0024010.0025420.0023460.0024000.0264330.00240.00223400
510.0332140.0023960.0024460.0023960.0025460.0024650.0024000.0252410.00240.00248200
610.0328320.0023990.0024660.0024040.0025790.0025100.0023960.0228990.00240.00246000
710.0324520.0024130.0024790.0024260.0026200.0024790.0023910.0216090.00240.00239800
810.0301860.0024390.0024540.0024490.0026300.0024310.0023950.0232470.00240.00240600
910.0272920.0024490.0024360.0024390.0026220.0024240.0024390.0258250.00240.00257200
semg_1emg_2emg_3emg_4emg_5emg_6emg_7emg_8emg_9emg_10replabel
46915100.0028680.0199110.0056260.0023920.0025400.0030790.0412910.2757500.1494580.01298400
46916100.0028380.0189440.0050830.0023970.0026240.0032450.0359240.2448020.1295490.01569100
46917100.0027030.0166450.0042830.0024000.0026430.0031590.0330430.2060890.1195400.01771400
46918100.0025940.0117680.0032600.0024000.0025580.0029960.0273090.1531680.1201060.01721800
46919100.0025230.0082330.0027790.0024000.0024590.0029520.0220430.1208440.1225290.01550300
46920100.0024910.0074230.0028410.0024000.0024030.0028420.0206480.1224850.1235480.01560900
46921100.0023500.0077460.0030340.0024010.0024010.0023850.0173230.1250970.1256550.01671800
46922100.0020150.0089090.0031260.0024010.0024460.0015050.0113830.1100750.1246530.01663400
46923100.0019040.0126110.0032390.0023960.0025210.0010590.0115100.0969980.1232070.01488800
46924100.0037070.0157250.0032730.0023820.0026060.0057750.0298880.0918360.1204130.01282700